Digital images are a necessary element in all modern medical imaging methods. Functions that can be performed with digital images include:

• Image reconstruction (CT, MRI, SPECT, PET, etc)
• Image reformatting (Multi-plane, multi-view reconstructions)
• Wide (dynamic) range image data acquisition (CT, digital radiography, etc)
• Image processing (to change contrast and other quality characteristics)
• Fast image storage and retrieval
•  Fast and high-quality image distribution (teleradiology)
• Controlled viewing (windowing, zooming, etc)
• Image analysis (measurements, calculation of various parameters, computer aided diagnosis, etc)

All of these functions are considered in detail in other modules.

Analog images are required for human viewing.

Therefore, all of the medical imaging methods that produce and use digital images, must convert the images to an analog form for display and viewing.

As humans, we cannot get much information from just looking at a display of many, many numbers, that is looking directly at a digital image.

A digital image is a matrix of many small elements, or pixels.

Each pixel is represented by a numerical value.  In general, the pixel value is related to the brightness or color that we will see when the digital image is converted into an analog image for display and viewing.

Generally, at the time of viewing, the actual relationship between a pixel numerical value and it's displayed brightness is determined by the adjustments of the window control as discussed in other modules.

A digital image is represented in the imaging and computer system by numbers in the form of binary digits, called bits.

Here we see the general structure of a digital image.

First, it is divided into a matrix of pixels.

Then, each pixel is represented a series of bits.

We will soon discover the issues that affect the number of pixels in an image and the number of bits per pixel, the so-called pixel bit depth. 

We can write ten different digits, 0,1,2,3,4,5,6,7,8, and 9, .  This system probably developed because we have ten fingers.

When we write larger numbers (more than one digit) the position of a digit within the number has a certain value, 1, 10, 100, 1000 etc as shown here.

The value of a number we have written is just the sum of the values represented by each digit position.

In this example, 8000 + 500 + 30 + 4 =8,534.

We have just reviewed that we as humans can write ten different digits.

Digital imaging systems and computers can only write two different digits.

They write numbers by filling in spaces (in the computer memory, on disk, etc).

However, they can only place a mark in the space (the black dot shown here) or leave the space blank (the white dot shown here).

Therefore, each digit space can have only two possible values, MARKED or BLANK.  For that reason, these are called binary (meaning two) digits, or for short, bits.

There is an advantage in writing in bits.  We do not have to be concerned with the computer's "handwriting", like we must do sometimes for humans.  It is easy and accurate to just make marks.

Remember our human digit positions have values: 1, 10, 100, 1000, etc.

The binary (computer) digit positions have values: 1, 2, 4, 8, 16, etc.

The value of a multiple-digit number is just the sum of the values of the marked digits as we see here.

It is as simple as that!

One of the limitations with using binary numbers is the range of values that can be written with a specific number of bits (binary digits).

We explore the process here by using four bits.  As we see, four bits can have 16 different values because there are 16 ways the four bits can be marked.

The range of possible values that can be written is increased by using more bits.  As shown by the equation, the range (number of possible values) is the number 2 multiplied by itself, or raised to the power, by the the number of bits.

The range of possible values is doubled for each additional bit used as we will soon see.

Here we have an example of using only four bits.

This is smaller than would be used in any actual medical image because with four bits, a pixel would be limited to having only 16 different values (brightness levels or shades of gray).

Medical images require more, as we will soon see.

Here we compare three images, displayed with different bit depths and possible brightness levels.

The first image is displayed with only one bit per pixel.  A pixel can have only two possible values, BLACK or WHITE.

The second image, with four bits per pixel, is limited to 16 different brightness levels (shades of gray)

The third image, with eight bits per pixel, can display 256 different brightness levels. This is generally adequate for human viewing.

The physical size of a pixel, relative to the anatomical objects, is the amount of blurring added to the imaging process by the digitizing of the image.

Here we see that an image with small pixels (less blurring) displays much more detail than an image made up of larger pixels.

Image size is the dimensions of the field of view (FOV) within the patient's body, not the size of a displayed image.

Matrix size is the number of pixels along the length and width of an image. This can be the same in both directions, but generally it will be different for rectangular images to produce relatively square pixels.

This will generally reduce blurring and improve image detail.

Also, with many modalities, the matrix size can be adjusted by the operator to optimize image quality and the imaging procedure.

A practical issue is that larger images (such as a chest radiograph) require a larger matrix (more pixels) than a smaller image in order to have good detail.

1. The number of pixels which is found by multiplying the pixel length and width of the image.
2. The bit depth (bits per pixel).  This is usually in the range of 8-16 bits, or 1-2 bytes, per pixel.

The significance of numerical size is that the larger the image (numerically), the more memory and disk storage space is required, and more time for processing and distribution of images is required.

There are many different mathematical methods used for image compression.

The level of compression is the factor by which the numerical size is reduced.  It depends on the compression method and the selected level of compression.

Lossless compression is when there is no loss of image quality, and is commonly used in many medical applications.

Lossee compression results in some loss of image quality and must be used with care for diagnostic images.

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